Job Details

Job Duties

We are building a platform that merges real-time physics, structured knowledge, and autonomous intelligence. We are looking for developers ready to tackle one (or more) of those challenges:

• Ingestion: Handling massive streams of sensor data with minimal latency.
• Intelligence: Making LLMs deterministic and reliable within robust multi-agent systems.
• Context: Modeling complex ontologies to map thousands of physical assets.

What You’ll Do

You will join a high-performance team, contributing to the Core Backend while focusing on one of the following distinct tracks:

Core Responsibilities

• High-Performance APIs: Build low-latency Python services (FastAPI) to serve live data to frontend and AI models.
• System Reliability: Debug complex concurrency issues and ensure production reliability in distributed systems.
• Rapid Delivery: Adopt a "deliver fast" mentality without compromising on code quality, testing, or API design standards.

Possible paths to work on the Project (you can contribute to one or more):

Streaming & High-Throughput Data 

• Build Streaming Pipelines: Design scalable services using Kafka and Spark/Flink to process raw sensor data in real-time.
• Time-Series Optimization: Optimize database schemas (TimescaleDB) to enable fast historical data retrieval and implement algorithmic checks to validate sensor readings.

GenAI & Autonomous Agents

• Build Autonomous Agents: Deploy stateful agents (using LangGraph) that plan tasks, query Knowledge Graphs, and execute tools without hallucinating.
• Advanced RAG: Build Graph-RAG pipelines that combine semantic search with structured knowledge traversal for grounded answers

Graph & Knowledge Engineering

• Knowledge Graph Engineering: Design domain ontologies in Neo4j, defining relationships between assets, documents, and time-series data.
• Search Infrastructure: Implement Hybrid Retrieval logic combining Vector Search, Full-Text Search, and Graph traversal.

Qualifications

Must Haves:

• Complete Bachelor's degree in Computer Science, Engineering, or related.
• 5+ years of experience in Python Backend development.
• Advanced English communication skills.
• Computer Science fundamentals: Data Structures and Algorithms.
• Strong experience building and documenting REST APIs (FastAPI).

Good to Have:

• Streaming: Proficiency with Streaming Technologies (Kafka, Flink, Spark) and Time-Series Databases (TimescaleDB, InfluxDB).
• GenAI: Practical experience building applications with LLMs, Agentic Frameworks (LangGraph), and Vector Databases.
• Graph: Hands-on experience with Graph Databases (Neo4j/Cypher), SQL database design, and Hybrid Search strategies
• Background in Heavy Industry, O&G, or IoT data (MQTT, OPC UA).
• Experience with Local LLMs (Ollama) for privacy-focused deployments.
• Familiarity with Data Lineage or Metadata management.

Knowledge, Skills, and Abilities

We use a modern, high-performance stack. You should be proficient in the Core and deeply knowledgeable in your chosen track.

• Core Backend: Python 3.12+ (FastAPI, Pydantic), Polars, Docker, Kubernetes.
• Streaming: Apache Kafka, Flink, Spark Streaming, TimescaleDB, Tiger Data.
• GenAI: LangGraph, LangChain, LiteLLM, Azure OpenAI/Anthropic/Local SLMs.
• Graph/Data: Neo4j (Cypher), PostgreSQL (pg_vector, Full-Text Search).
  

Desired Profile

• Problem Solver: You dig into logs to find the root cause of a data spike, a silent failure, or a disconnected node in a graph.
• Modern Pythonista: You are up-to-date with modern Python async patterns and typing, ensuring code is high-performance and maintainable.
• You understand Big O notation and how to optimize code for CPU/memory efficiency.

Job Duties

We are building a platform that merges real-time physics, structured knowledge, and autonomous intelligence. We are looking for developers ready to tackle one (or more) of those challenges:

• Ingestion: Handling massive streams of sensor data with minimal latency.
• Intelligence: Making LLMs deterministic and reliable within robust multi-agent systems.
• Context: Modeling complex ontologies to map thousands of physical assets.

What You’ll Do

You will join a high-performance team, contributing to the Core Backend while focusing on one of the following distinct tracks:

Core Responsibilities

• High-Performance APIs: Build low-latency Python services (FastAPI) to serve live data to frontend and AI models.
• System Reliability: Debug complex concurrency issues and ensure production reliability in distributed systems.
• Rapid Delivery: Adopt a "deliver fast" mentality without compromising on code quality, testing, or API design standards.

Possible paths to work on the Project (you can contribute to one or more):

Streaming & High-Throughput Data 

• Build Streaming Pipelines: Design scalable services using Kafka and Spark/Flink to process raw sensor data in real-time.
• Time-Series Optimization: Optimize database schemas (TimescaleDB) to enable fast historical data retrieval and implement algorithmic checks to validate sensor readings.

GenAI & Autonomous Agents

• Build Autonomous Agents: Deploy stateful agents (using LangGraph) that plan tasks, query Knowledge Graphs, and execute tools without hallucinating.
• Advanced RAG: Build Graph-RAG pipelines that combine semantic search with structured knowledge traversal for grounded answers

Graph & Knowledge Engineering

• Knowledge Graph Engineering: Design domain ontologies in Neo4j, defining relationships between assets, documents, and time-series data.
• Search Infrastructure: Implement Hybrid Retrieval logic combining Vector Search, Full-Text Search, and Graph traversal.

Qualifications

Must Haves:

• Complete Bachelor's degree in Computer Science, Engineering, or related.
• 5+ years of experience in Python Backend development.
• Advanced English communication skills.
• Computer Science fundamentals: Data Structures and Algorithms.
• Strong experience building and documenting REST APIs (FastAPI).

Good to Have:

• Streaming: Proficiency with Streaming Technologies (Kafka, Flink, Spark) and Time-Series Databases (TimescaleDB, InfluxDB).
• GenAI: Practical experience building applications with LLMs, Agentic Frameworks (LangGraph), and Vector Databases.
• Graph: Hands-on experience with Graph Databases (Neo4j/Cypher), SQL database design, and Hybrid Search strategies
• Background in Heavy Industry, O&G, or IoT data (MQTT, OPC UA).
• Experience with Local LLMs (Ollama) for privacy-focused deployments.
• Familiarity with Data Lineage or Metadata management.

Knowledge, Skills, and Abilities

We use a modern, high-performance stack. You should be proficient in the Core and deeply knowledgeable in your chosen track.

• Core Backend: Python 3.12+ (FastAPI, Pydantic), Polars, Docker, Kubernetes.
• Streaming: Apache Kafka, Flink, Spark Streaming, TimescaleDB, Tiger Data.
• GenAI: LangGraph, LangChain, LiteLLM, Azure OpenAI/Anthropic/Local SLMs.
• Graph/Data: Neo4j (Cypher), PostgreSQL (pg_vector, Full-Text Search).
  

Desired Profile

• Problem Solver: You dig into logs to find the root cause of a data spike, a silent failure, or a disconnected node in a graph.
• Modern Pythonista: You are up-to-date with modern Python async patterns and typing, ensuring code is high-performance and maintainable.
• You understand Big O notation and how to optimize code for CPU/memory efficiency.

Job Duties

We are building a platform that merges real-time physics, structured knowledge, and autonomous intelligence. We are looking for developers ready to tackle one (or more) of those challenges:

• Ingestion: Handling massive streams of sensor data with minimal latency.
• Intelligence: Making LLMs deterministic and reliable within robust multi-agent systems.
• Context: Modeling complex ontologies to map thousands of physical assets.

What You’ll Do

You will join a high-performance team, contributing to the Core Backend while focusing on one of the following distinct tracks:

Core Responsibilities

• High-Performance APIs: Build low-latency Python services (FastAPI) to serve live data to frontend and AI models.
• System Reliability: Debug complex concurrency issues and ensure production reliability in distributed systems.
• Rapid Delivery: Adopt a "deliver fast" mentality without compromising on code quality, testing, or API design standards.

Possible paths to work on the Project (you can contribute to one or more):

Streaming & High-Throughput Data 

• Build Streaming Pipelines: Design scalable services using Kafka and Spark/Flink to process raw sensor data in real-time.
• Time-Series Optimization: Optimize database schemas (TimescaleDB) to enable fast historical data retrieval and implement algorithmic checks to validate sensor readings.

GenAI & Autonomous Agents

• Build Autonomous Agents: Deploy stateful agents (using LangGraph) that plan tasks, query Knowledge Graphs, and execute tools without hallucinating.
• Advanced RAG: Build Graph-RAG pipelines that combine semantic search with structured knowledge traversal for grounded answers

Graph & Knowledge Engineering

• Knowledge Graph Engineering: Design domain ontologies in Neo4j, defining relationships between assets, documents, and time-series data.
• Search Infrastructure: Implement Hybrid Retrieval logic combining Vector Search, Full-Text Search, and Graph traversal.

Qualifications

Must Haves:

• Complete Bachelor's degree in Computer Science, Engineering, or related.
• 5+ years of experience in Python Backend development.
• Advanced English communication skills.
• Computer Science fundamentals: Data Structures and Algorithms.
• Strong experience building and documenting REST APIs (FastAPI).

Good to Have:

• Streaming: Proficiency with Streaming Technologies (Kafka, Flink, Spark) and Time-Series Databases (TimescaleDB, InfluxDB).
• GenAI: Practical experience building applications with LLMs, Agentic Frameworks (LangGraph), and Vector Databases.
• Graph: Hands-on experience with Graph Databases (Neo4j/Cypher), SQL database design, and Hybrid Search strategies
• Background in Heavy Industry, O&G, or IoT data (MQTT, OPC UA).
• Experience with Local LLMs (Ollama) for privacy-focused deployments.
• Familiarity with Data Lineage or Metadata management.

Knowledge, Skills, and Abilities

We use a modern, high-performance stack. You should be proficient in the Core and deeply knowledgeable in your chosen track.

• Core Backend: Python 3.12+ (FastAPI, Pydantic), Polars, Docker, Kubernetes.
• Streaming: Apache Kafka, Flink, Spark Streaming, TimescaleDB, Tiger Data.
• GenAI: LangGraph, LangChain, LiteLLM, Azure OpenAI/Anthropic/Local SLMs.
• Graph/Data: Neo4j (Cypher), PostgreSQL (pg_vector, Full-Text Search).
  

Desired Profile

• Problem Solver: You dig into logs to find the root cause of a data spike, a silent failure, or a disconnected node in a graph.
• Modern Pythonista: You are up-to-date with modern Python async patterns and typing, ensuring code is high-performance and maintainable.
• You understand Big O notation and how to optimize code for CPU/memory efficiency.

Job Duties

We are building a platform that merges real-time physics, structured knowledge, and autonomous intelligence. We are looking for developers ready to tackle one (or more) of those challenges:

• Ingestion: Handling massive streams of sensor data with minimal latency.
• Intelligence: Making LLMs deterministic and reliable within robust multi-agent systems.
• Context: Modeling complex ontologies to map thousands of physical assets.

What You’ll Do

You will join a high-performance team, contributing to the Core Backend while focusing on one of the following distinct tracks:

Core Responsibilities

• High-Performance APIs: Build low-latency Python services (FastAPI) to serve live data to frontend and AI models.
• System Reliability: Debug complex concurrency issues and ensure production reliability in distributed systems.
• Rapid Delivery: Adopt a "deliver fast" mentality without compromising on code quality, testing, or API design standards.

Possible paths to work on the Project (you can contribute to one or more):

Streaming & High-Throughput Data 

• Build Streaming Pipelines: Design scalable services using Kafka and Spark/Flink to process raw sensor data in real-time.
• Time-Series Optimization: Optimize database schemas (TimescaleDB) to enable fast historical data retrieval and implement algorithmic checks to validate sensor readings.

GenAI & Autonomous Agents

• Build Autonomous Agents: Deploy stateful agents (using LangGraph) that plan tasks, query Knowledge Graphs, and execute tools without hallucinating.
• Advanced RAG: Build Graph-RAG pipelines that combine semantic search with structured knowledge traversal for grounded answers

Graph & Knowledge Engineering

• Knowledge Graph Engineering: Design domain ontologies in Neo4j, defining relationships between assets, documents, and time-series data.
• Search Infrastructure: Implement Hybrid Retrieval logic combining Vector Search, Full-Text Search, and Graph traversal.

Qualifications

Must Haves:

• Complete Bachelor's degree in Computer Science, Engineering, or related.
• 5+ years of experience in Python Backend development.
• Advanced English communication skills.
• Computer Science fundamentals: Data Structures and Algorithms.
• Strong experience building and documenting REST APIs (FastAPI).

Good to Have:

• Streaming: Proficiency with Streaming Technologies (Kafka, Flink, Spark) and Time-Series Databases (TimescaleDB, InfluxDB).
• GenAI: Practical experience building applications with LLMs, Agentic Frameworks (LangGraph), and Vector Databases.
• Graph: Hands-on experience with Graph Databases (Neo4j/Cypher), SQL database design, and Hybrid Search strategies
• Background in Heavy Industry, O&G, or IoT data (MQTT, OPC UA).
• Experience with Local LLMs (Ollama) for privacy-focused deployments.
• Familiarity with Data Lineage or Metadata management.

Knowledge, Skills, and Abilities

We use a modern, high-performance stack. You should be proficient in the Core and deeply knowledgeable in your chosen track.

• Core Backend: Python 3.12+ (FastAPI, Pydantic), Polars, Docker, Kubernetes.
• Streaming: Apache Kafka, Flink, Spark Streaming, TimescaleDB, Tiger Data.
• GenAI: LangGraph, LangChain, LiteLLM, Azure OpenAI/Anthropic/Local SLMs.
• Graph/Data: Neo4j (Cypher), PostgreSQL (pg_vector, Full-Text Search).
  

Desired Profile

• Problem Solver: You dig into logs to find the root cause of a data spike, a silent failure, or a disconnected node in a graph.
• Modern Pythonista: You are up-to-date with modern Python async patterns and typing, ensuring code is high-performance and maintainable.
• You understand Big O notation and how to optimize code for CPU/memory efficiency.